{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.optim import lr_scheduler\n",
    "import numpy as np\n",
    "import torchvision\n",
    "from torchvision import datasets, models, transforms\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import os\n",
    "import copy\n",
    "\n",
    "plt.ion()   # interactive mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_transforms = {\n",
    "    'train': transforms.Compose([\n",
    "        transforms.RandomResizedCrop(224),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "    ]),\n",
    "    'val': transforms.Compose([\n",
    "        transforms.Resize(256),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "    ]),\n",
    "}\n",
    "\n",
    "data_dir = 'toydata'\n",
    "image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),\n",
    "                                          data_transforms[x])\n",
    "                  for x in ['train', 'val']}\n",
    "dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,\n",
    "                                             shuffle=True)\n",
    "              for x in ['train', 'val']}\n",
    "dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}\n",
    "class_names = image_datasets['train'].classes\n",
    "\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4, 3, 224, 224])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs, classes = next(iter(dataloaders['val']))\n",
    "inputs.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['NoToy', 'Scenes', 'SingleToy', 'NoToy']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[class_names[x] for x in classes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([ 0,  1,  2,  0])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset ImageFolder\n",
       "    Number of datapoints: 24\n",
       "    Root Location: toydata/val\n",
       "    Transforms (if any): Compose(\n",
       "                             Resize(size=256, interpolation=PIL.Image.BILINEAR)\n",
       "                             CenterCrop(size=(224, 224))\n",
       "                             ToTensor()\n",
       "                             Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
       "                         )\n",
       "    Target Transforms (if any): None"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_datasets['val']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['NoToy', 'Scenes', 'SingleToy']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_datasets['train'].classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def imshow(inp, title=None):\n",
    "    inp = inp.numpy().transpose((1, 2, 0))\n",
    "    mean = np.array([0.485, 0.456, 0.406])\n",
    "    std = np.array([0.229, 0.224, 0.225])\n",
    "    inp = std * inp + mean\n",
    "    inp = np.clip(inp, 0, 1)\n",
    "    plt.imshow(inp)\n",
    "    if title is not None:\n",
    "        plt.title(title)\n",
    "    plt.pause(0.001)  # pause a bit so that plots are updated\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def train_model(model, criterion, optimizer, scheduler, num_epochs=1):\n",
    "    best_model_wts = copy.deepcopy(model.state_dict())\n",
    "    best_acc = 0.0\n",
    "    for epoch in range(num_epochs):\n",
    "        print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n",
    "        for phase in ['train', 'val']:\n",
    "            if phase == 'train':\n",
    "                scheduler.step()\n",
    "                model.train() \n",
    "            else:\n",
    "                model.eval()  \n",
    "            running_loss = 0.0\n",
    "            running_corrects = 0\n",
    "            for inputs, labels in dataloaders[phase]:\n",
    "                inputs = inputs.to(device)\n",
    "                labels = labels.to(device)\n",
    "                optimizer.zero_grad()\n",
    "                with torch.set_grad_enabled(phase == 'train'):\n",
    "                    outputs = model(inputs)\n",
    "                    _, preds = torch.max(outputs, 1)\n",
    "                    loss = criterion(outputs, labels)\n",
    "                    if phase == 'train':\n",
    "                        loss.backward()\n",
    "                        optimizer.step()\n",
    "                running_loss += loss.item() * inputs.size(0)\n",
    "                running_corrects += torch.sum(preds == labels.data)\n",
    "            epoch_loss = running_loss / dataset_sizes[phase]\n",
    "            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
    "            print('{} Loss: {:.4f} Acc: {:.4f}'.format(\n",
    "                phase, epoch_loss, epoch_acc))\n",
    "            if phase == 'val' and epoch_acc > best_acc:\n",
    "                best_acc = epoch_acc\n",
    "                best_model_wts = copy.deepcopy(model.state_dict())\n",
    "        print()\n",
    "    print('Best val Acc: {:4f}'.format(best_acc))\n",
    "    model.load_state_dict(best_model_wts)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def visualize_model(model, num_images=6):\n",
    "    was_training = model.training\n",
    "    model.eval()\n",
    "    images_so_far = 0\n",
    "    fig = plt.figure()\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for i, (inputs, labels) in enumerate(dataloaders['val']):\n",
    "            inputs = inputs.to(device)\n",
    "            labels = labels.to(device)\n",
    "\n",
    "            outputs = model(inputs)\n",
    "            _, preds = torch.max(outputs, 1)\n",
    "\n",
    "            for j in range(inputs.size()[0]):\n",
    "                images_so_far += 1\n",
    "                ax = plt.subplot(num_images//2, 2, images_so_far)\n",
    "                ax.axis('off')\n",
    "                ax.set_title('predicted: {}'.format(class_names[preds[j]]))\n",
    "                imshow(inputs.cpu().data[j])\n",
    "\n",
    "                if images_so_far == num_images:\n",
    "                    model.train(mode=was_training)\n",
    "                    return\n",
    "        model.train(mode=was_training)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_ft = models.resnet18(pretrained=True)\n",
    "num_ftrs = model_ft.fc.in_features\n",
    "model_ft.fc = nn.Linear(num_ftrs, 3)\n",
    "model_ft = model_ft.to(device)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)\n",
    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ResNet(\n",
      "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
      "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (relu): ReLU(inplace)\n",
      "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
      "  (layer1): Sequential(\n",
      "    (0): BasicBlock(\n",
      "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    )\n",
      "    (1): BasicBlock(\n",
      "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    )\n",
      "  )\n",
      "  (layer2): Sequential(\n",
      "    (0): BasicBlock(\n",
      "      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): BasicBlock(\n",
      "      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    )\n",
      "  )\n",
      "  (layer3): Sequential(\n",
      "    (0): BasicBlock(\n",
      "      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): BasicBlock(\n",
      "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    )\n",
      "  )\n",
      "  (layer4): Sequential(\n",
      "    (0): BasicBlock(\n",
      "      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): BasicBlock(\n",
      "      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    )\n",
      "  )\n",
      "  (avgpool): AvgPool2d(kernel_size=7, stride=1, padding=0)\n",
      "  (fc): Linear(in_features=512, out_features=3, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(model_ft)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/0\n",
      "train Loss: 0.8916 Acc: 0.5726\n",
      "val Loss: 0.4940 Acc: 0.8333\n",
      "\n",
      "Best val Acc: 0.833333\n"
     ]
    }
   ],
   "source": [
    "model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,\n",
    "                       num_epochs=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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whNkaC626pSOT5hQOkUgdLxhObtEfHVyVzdg17CRRgouoKlMqjONErZlnT+5Q\nLFEk40RZhI5F1zMMwxwsb80vA3jvCcE34lylm7YKA1YKMhfr9D6y7HqG4OgjdEGIrskxlwSxkjFa\nrnEIwnLZsRgiaXWAWYumr1ZBDScBH2OriN11eOcJwc3125gzjSplarlCIq3Kgp69hbt/l7I+x4XF\nUxz5J2MniXIZie5U0eDb072BlDJh8FdlQbsYGYaBECPBhSYfltQCsqWF1KsZpjCVdrOKQCkVyxnt\nlRgj0QteIEgrMXoZ9Xbp62nhk0LJuVVvqkbsPCpxzlZrlVNUhBgibpZzvPctD8la3f5KpZY6V22C\nrML2nddZDYUYe6SWq2SzXcNOEsXF7qpe/EHscDVSayZoKwgc5lLlMXr66On7js7FdgNKbMsRuSWK\nFSPXQqq5OQ1TZjNuW/kJLXRdwHnwKFJbZWtRbTesQLVCzpVqrW5sqWkuLdqWMxVB8XNpc0dwoVXL\ndoqTirpWrd+sUmorzJRpOT9ahVe+/Mvc/sxnWvlQ16o17CJ2kihCyyV2CAe+w1fhrJ4S3QFYQrQV\n3guhvYEjIkRasJA5RRzU7DEH6dKoJZmKMKaJzWZkCJ7oO5z3LV+ozE9zNXwQclakTtRSSVNmqoAV\nbJpa5SWJtPyguUabg9bjClmA1Kp7uZYHJFRIza90WaypmvBWToybC7YXZwz9EWb74OobQ83RS6Ay\ntrwqgdX9A1791Zc4uf8cWg3qBMlhU3vBp4qiteX8UIEwF6ZxrXxGMRjTxNl6ZKqVlffEGPACmisp\nbelt2YKapEPChJZWSTJNmfFi3cIia0GphG5AVeljhznXihd7o4RW5bqm5r1WL+AdRVsUm9VKrgWr\nhVIyL/sF9z/3q/hwzPLucwTfPdWxfxJ2kiguFdSBqW/ZgiVDEFbP3cJyoaREGkemklutlJIpzA81\nzRBnvlWCrNdyitOUsFIYQk8XWinyUjI5uRYju93Sl4LOVSlRCCGSQ8E7Zdwm1mePUTFiHOlCD3Fq\nWlXusdhBNsR51Ax1SiUgxcDPedAlkWsTglsNlsRPmhL/0c9w7+EnW3XIHcRuEqUEtBRqq+JKMpjG\nCbfoKI8S0zozGoTOM/otCsRSSUMhxA7x2vSLDKUmUkqsNxecP35EyYkuduQ88eDhAxbekRcLqhlT\ngSlPxL6fheVujlMJHCwHqhQePXqLtN3g9IIYOoY4EJ1SnCMuV1gcsK7lGTkJWG6pJi1bcSZHrpRt\nptSKeLhI8L+vz+hf/lXA8T1Pd/jfFztJFMmCFQhOSVaRorjicdYqCGxzoU+ZRGUMG4KDrXV007ZZ\nYWm/r9JufBo3TLmwnRJlSpieggOIAAAIsklEQVQKGzPG7QWaJm6vlpRc6MrIZv0Y1UoMA8vViqOT\nW3hxzUkJhH7BxWaEszOSW1MXE0U9EiKkgltOiC1bUpdlNEQ0QM21FQaaqyuUcWq1+OXjePdKK76D\nXqnSu4adJApmlG2rJY8KwWy2XkJyju20Zl0rzge8KFoqEkcoEbSi6qnqSALrx495+PgRD9dbLs4e\ncrFdk22Dl8hUCg8en3FrteSFnDicTvAhsr3YoptzDpcH3HvmHovDE4oLmDjiYoXfjLzz1utoNdK4\nJYeIxUiJy9k6XKlDQlKHxow6D2Lk2T6TcianxDZnPv9L58Tjb2ZxcMTy4CEl//zTHv33xU4Spc5v\nLClW8c5RDab1SCkF10XGktmUgtZC3AquZrx1iCouVVxoBfayFbbTxJRbaijiqTkzmeM8PWTKhuXC\nmEfCENty5hx1PVHPL3hnfIXHD17h2Y98I8fPvIgbBqLzLFbHvBOXnD14E6sZGRY4aW/v0qmFD5hW\nXEk46xHfzdURlCKt7FfCSCmzffBl6mxNntIht2/9jqc9/O+LnSRK2m4pqeB0LkBTmyqZi5CnlrB1\nvtlgJRMFpASCOjSCbDeEGikhMNVCMkNCT6iBo0PPYjhAnWPaXrBer0njFjPl4vQcSRP37j/P4Yt3\n6bQjlMrx3Xsc3r5H1/WUtCWgHK5u88zz38BXq/HwtS+TLs6xk+Nm9Xf+KgsxxNDeASRr3DwjVRVy\nrUw5sxgi98ObvPog4tyiaVxpHwp5Y0iuOBSnQrxWW80sUQV86Dg7ewxmRGn2kz5ndLtFnDZtySYu\nykSqNldmalH6Qx8R51ksOg4OV4gJznXE6OicsFwtWSyOWS5XLOLA8d17xNjBtMGFHpkShtEvBo7u\n3ufxgzc4W5+1WFsE53tMBRvbjObnLMQyTVRRzLVkM1MhDp7v+57v5of+p7/Fen0fwTFt9iXObwzv\nBPUeRJvDTVuebgwtveJw2XH24C0u0kSA5odxrUqBiRByYgtcpJHRlJIqJbXXsLgu4NURnDDEjtBF\nYmxaTs0TvhsQ5/Ahot2lid6B75rayxaXMzEEDlZHrO48y1svn8PFmuIDFh+zyBO5XxBVcOMGH7p3\nfUyhzTZ1tip/0yc/xW85nvgFKgXfSLmD2EmiuOCvHGZz1fimISh0wWOqHK6OePDoHdbTFqVwOrqr\nakmudJzXRBHPVArjOOIApz1+LjOqoSOI0sVI7FpsyrTdUrbN/mHTEkJHSYlJBacBrZXqWsSbqBBc\nYLk65OzwFo/feKVVVnIO6yqmSlEINbSyYtVQ38OUEO9aTG8MaHB86z/7T/MPf7bgAuS0fw3LjeG9\nawX4CmzzhM7OuoU6NARuv7zhk68O/F11vNOB5Qm3XVNqbe/wofDw7DFTCIzrkVJpNpHOkKBIiARA\nYyR2PcvVMbFbsI0r1o/eZrteszxoZUdLrUiu0M2lwsyoKhRVMhUXA0d37nFxdsrZ+QUqkBdLcs30\neUnwPT4GYugxUtPignK47FFXkWrce+Gj2Gc/T5WBnKenPfzvi50kikp7xVoprSxFshbzkQN87Kff\nQc42DL7jm+WEn0xvk4DJCoEWVhiWC5Y1US7OefjwAak6FgeRxVxxKedKjXNMbhzQonTiCYsTJBs2\nV0vIyRiniWqgOMwK42ZNKiPnmw1npw+5ePyIlLd0iwXbMmEaGKeCC2PzgveuvelLKpSJuDzAnGe5\n7KilKczeufYKmHHbIvR3EDtJlJQKtbZl4fDByLOvFMKYeHj+kMkqySpvr085H9csnxHOyay37a1g\n6oROHQfPvshRzRT9Nb7y1dd59Khw4SZOH64RqQRTvDqOu45bR4fcu/scw2JB3qyp2zVnb79Jtzqi\nOzoi9BENfSumkybWm1MePnzAw3feoKQJ7x19HwnB8METu4iVwpgyvq8t/MkqPkbMhEWv+E5RiZRq\nnJ1dkFIhy2ZvcPvHg+B94M5rW06+9AjB8yCNnFZjnTYMVdlMG+g89mhNve3YpoI733DeneGq4mIk\nBMetu3dZbxJn52tKMtbbC+pklG2CarxaCoMqt5ZfZBgG3OXbumomek8cDugWA/3BgrhYsTi+je8C\nUYXVQc9mUwnOcbA8oK9G13cshgN8Le21uurmfCGHcx6RxOHhCZYE64WSE48etYBqKxOW997jG+PO\nyyOHr17wVhr5lXTKXdcTRfHV6A0elDUbmzg/H9l0kcWjyOntLSkX/MMHpJpJWomLHjU4PlrRDQMF\nY9xMWKp0LiIJ0ukayYVjHB2RoT+A6Mnnjxj8gvsf/xb6O8/gFg4XBd8bNW2oeFI95o3XvsL29ALN\nheXhEVEdXYg455o7wXeIc6jzpGo899wxIfqW/WiV7Do+9+W3mnm/VOrsDd817CRRyisbvpS2HJbC\nuLng58Y3KLMBPFdBh0UrS+6FF83xhYtzHnSR2wdwlgtusyGOC4rTq/fvqCjqBLcM5JToQkeUQH/n\nNmwTcb2l957oezR4fN/Rd5GDlccvEmHVgqPFGWhEukNS3nJwuGbarNmen6HDAHFBdRXXD+AdQlPz\nUy0cHvXEPiLBt6qQPrKeKl94fUvODq+OtH+T+s3x2qOv8s72MS+FyKut5AB3iaxKaX6dCqd15EUd\nmGrh0Du+cAZTgudPlIsp0V+sOQgdGkKrCZs35ATOhfltGIXklWHZMdw5Zuk8LldIGe9dS+Y6OEBP\nVrhuwPvw7ruXo2Eh4KWwWC7Jx7c4e+tNxjwh6puF1nfNiTm/Y+i5F+6yWnUQZC5EHNDY8wu/9Dne\n2Q4oiSzg4m5mCsquvppsj93CbkpOe+wc9kTZ40bYE2WPG2FPlD1uhD1R9rgR9kTZ40bYE2WPG2FP\nlD1uhD1R9rgR9kTZ40bYE2WPG2FPlD1uhD1R9rgR9kTZ40bYE2WPG2FPlD1uhD1R9rgR9kTZ40bY\nE2WPG2FPlD1uhD1R9rgR9kTZ40bYE2WPG+H/A7gNzxnmh5DpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa7f869f940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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F9ASjd99ALelAp8vxE88xMrSTU8cucvCm20mCWUrl/QiRwtcx0l6lf2CcqeUavb0S6dRJ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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa7f45227f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5zs2onolSSJKfmuS3ofPov/fj/OCh33LBZX+HmZ+gmM9jlBzGJ0M0\ntNiYro8nSbz46E42X7KRn34mwds+k2fvoVFWv+kqnrj5Pi68OU5x6Dif/HYczx8nUeWyoqtIMtOA\nLiXJpkv4hoxmeTgBGcPwKBUzXHRuiCfdEJLtoNg2Dc3z6D6yg0g0QXJygkP9UyiSREVQJ6RpeJIF\nGihy8KT4/JRI9WOp44xNDDA1Mc6Td19OKu1jlGxyRZ+3v7OOY2N1HEu10ty1gPWtjRimzxq/l492\nZknnLTwphpofItrSQlAICvkMu/YdJBh2aVi2GCmyALs0huZJ7NryMstWJ/jYv+/nxluO88MPT7Kk\nOUv/sXuxXZ9QRAbfxbHKGKaFamRYMT/G9MhxjHyBt31qLd6qGFVrZL5/+AZGDz5LJmvwt6+D21/3\nMn/T1cu8jk5KZpmrXn8VC5cuZaZUYvbkvCCsangCVE8geTK1+sn51atTIuLL5RSuC5ouUzZlfv6N\nzVz9xgf48S/iTOYmoHEl9W9LEI2E+HzTIfx2gRpYjeM5nLt0gqv+7nrGkiVqLjoPDJOKcIJ1K5cz\n0b+NeMtK9FA9AyMOxfxROhYFecf/shgyI2hymO/8wuUtF04xcNxG02TcskZTIMW7338TWrCSnsM/\n4BcP7qP1/mne9IF6Wjsbufq2TnCK7N31GH1909z6hQLv+XsN2zApHxRUxOZRLR8jXWihv3+YyaKP\nLCQUSaJkGgS1CLbroMoyjncan6uPxisplQwkCZKpbsrlSSaTaa64NEnSXMPjlZ8ERaM43M1MtJfK\n+jb0QBhdDqI4Ze753pd4/VcP8unPvhElGKYwNM7oSD9NzZ042NjWOJJVor4+yjtvixJrrWddMI6X\nneQ9V/Ywk1LRdA3TClIuF2iKKqxYsJJwKEpm8jKG04cR0y7lbJF82cDKZzHLZXxJcM/dDumCxN7u\nCC2VLkcGfNZtVGntXMv8RYv59cMPkSuCIgtkWYDrY3sOeiRCSJWxiqdxcXd8/Oe4+lUw7VAy+7D8\nHg4fdjBNl+gbP0ulX4nvmIwkC+SlBAlJQkgSkiyjhmNU17ex9dYGTE3Bcgy0uij18hKs5CDFzAQF\nu4O6gMbDj5/LhhVDDBcCVJae5Q2XqxRmCmiey8tTOve8VIdnDmDVNhJRJCyjSGfrPFbEGlh+/hCV\nrXHG+if48r9muPAahc2XxjnrTJcXcm28MOHRNmNRFDItNRG6Vm7mV7+8CxGsZLK3h3hYQ0egCgnH\n9YiEo3h4qPGT8zXpU4J4xxnhWPedhKNtFHN9IPtY2zUkyyVxbR2eCqO/+B6JC19Psgztsgyej/B9\ntEAIWWtiJj2DcF3Mcp5kMolrGgSERmXHYoKORXZfLZ5qUGEbrD53Nc2Fl8nnCiieQJPj/Py5GNli\nEseXGSl76LKK5NiUZlKoSpK2eTpTIxl+/WuLEclGiwps10L2bbymVmZsyO0a54KNq0GS2H/oMJoM\nh4/3zxZS7uy/HPu6TDQYBtcBRUY9SZ/SnBLEq5qCaToUC70geyjhOFRUIWSZfNIlXGVTdcVbKEyO\nUxuVsA0LggJcDyWgo6AhhIRvG5hmCStfJlZXRab/EPH6FoKBOtQ1X2BV4F5aAy8wM3oHecPCt+Ar\nW+rJydXc98tf0Dt0nBvedwOfuelDVDaG8GSNRn0Bl6vvonffN4nHTbbuAjwNSVikR7NMp6G+PMHE\nvmFaKivRVQnhSzzyyBOokQCG5RDQNIQiU6Nr+IqCKqkoqgJI+OI0Jj4cDiHLJp7n47k2nUuW4Rg2\ngVCAUdMgaAUwcmmUssW+sTgRvUzM91GEAN/FNh0co4RRLFG2DFBkclNT2OUi4XgULRAhEAoQr/kn\nCtNXEkj007P1YX67e5x9I6P89sHfUCrmcA0HxzbxLZNgOIQry7Q2NTE13cmBnMdMXkWqrqJtfgQR\nGGV8rEx+0ic3MoHmG8Tj1Vx2yYXkJ5O01Bzn6HQOIQlCqiAoPGJBDVtWyNs2EV1F9XzU/88/oP91\ncEoQ7xbzWJaMkCVcD0ClYE6RiNdil2Yo532sYgHPyPC9HSpndkQp2gYuNv7wBKVoAhcwrBKe6xOJ\nVZCdHCBe1UggVommKigeBIkTq6gCcQaNK1/HecU8NjKpmRI9x3vI5bJ884tfYd2y+fhCYJbKOLZF\nW/tCjo4F2R1ai7S6kmlzkuRIP73dEnu6PYSWJxGPsb65gv3P/oZV516DaQnGp3JEg0Hiukw0HAcB\nuqTgB0DzJCQFHHFydnBOCeK//pXP0ntkLw889jxjWZP+o4epqKpl4NhBpNYytqLimTa2K+ibmGEg\nFUCTs2SmhqibvwjfAdMoIwkZy7FRhIGmyLR0LUNFAklGxscHfEkDzycYCBAMhkAJEK8wmBwZQ7Jt\n9HCU6VwBzSlj2R5lW6JsOuzON4NbwkuXyE2NcveMD3Ok6ZJCLOATbl/L4rYo3Xu2sX94lELRoKOq\ngpiuUBagyDKG56HJGpbvoLgCcTqv8Uf2bePM89/AyLFBVmw6EzM9xg8efJa8C9rIc5jWKhxJxi/n\ncB3BE71V1MlpamqbKZYtTNvEymWxbIvZIy0BaptaUQMBPLMEroSs6EiKjOeBL4GihUAo+JJELp8l\nUhsiXB1kuL8HixrCgWrKpSLTRYeB0THEYC/xiiokVSadm0ZWFSzTQtVlqiMKbS0Lmeg/SmPVYvqm\nk4ynClSHg+i6hgF4CGwfhAeu8FAkCVcSxE7niN/z8nHGJn5Mx/zFHOju5ZKLrua8gV7OOftSXj42\nzNMjh+kPteI6JrIhePnAOG+YX8XE8CBaNAG+hZAlcEBCoMgKkWgcXAdXEUiej1nOIWQdRQ0gIeO5\nPpLs4btg2zZ4NsVsGlmWMItFJjMa0UCAqfERctkMtbUayWQWRdfQtCDlchkkkIREQJNorK0Fv8RL\nL+1j7/E+dEWlIhjEE4KyaaHpAoGCJ83WJlmjTGUkhn2SNk9PCeLzEgQsi3ueeoqq/9PevbzYTcUB\nHP+eR5Kbe+/ceXQqhVrpUAalL7QURcVu3LrR/8G1/4XgSnAnQkU3ogtxZQUXLkQKtWWKfTnt2M60\nnZl7Z27vKze5yUnOiYvp39AOJJ9/IJAvOXDOLyEdRffJY0wqyK5e59IHH+GSP/nkyCMuvXeRTy9H\nfPHxaR7cuo6m4PipFbAGSYmzgiiasHJylSw3WOfQzhF4PiAQQGFTsCDw0I2QNI0o8wwPB9aAhHg8\nYqAlYtkjV5rhJCa3Oa+e8OkOfGw6IwxDsjRBaY1sLVMKwbG25ubWM6Icji/OIYUizwu0khgnaWDR\n2qNwlsVmi5f5avuhCF9YwcPNbebn5hiOE6zyUWkXV8xx9+bvbG33maUBT5/EfPvZeb758nPef/cs\nUxfQ7+4hXIHE0ggbhIHPLEvQE0mr1YLSR9gcpEIoi1TBwRlAoEnjCJNExHGEKRxKtZgMevT3ehQ2\nozXXIWg0uL1+lzGSwGRo0SYrLEVZgHN0wpBSeJw81mFnc5fQN0gg0D4FgLMHf7sQAqE1JSWeVDhK\nfClwpXsp9/xQhM+iIX5mkKGP3+4w2t9GKckgmnHn/iO8oEVGmxs3r2KDJqIN/sJR9neH/PvHFVZO\nn0UmE5aPncDzA2ZpRjxp42nF0uIijWYT4fk0wjZCOwTgTE6cJGQmJk1ToskIE8dgc5QEcsvo2YBo\nOGDt9hpOQZJYkmgApaTZmiOZjvA9hXCWV44eIc8tV//5m2ajTVpysOIoDSX4UiJKcM+HNeQlDsvB\nxV68QzGd66sTPIoKdoYzymmPtjZ4pWF/mKKkT0PmJHHG1l6fK7/+zNbWFpe//oqN9VsUKCbDlNWL\nH/L9jz+RmozN+/fpdrsMRkN29rrsdncZDwZEozHT0YDezi7K09h0wuDZPv1+D5MZ4jiCsqSzsIQA\n8iyh39smKqYIpRHPnxPnHLN0hu8HhM2ASxfO89faDbRfMLUWZw3GOqTwcFbiSU2gPYrSMcvNwW7A\nU0gpyVyF9/HJeAfjHKXSTIRHHI1p+Y7Z7lOOhBqTN4iSKWkWEgQtpjmkbp5yUuJlEQ83fqNUijMX\nLrC2vs/6nWucPNrmnYtnuLbZZ+X4aywsLxO228wvLOH7Ib3tx2ANwhX4LsfYHF9JjBSUuSWdjPBL\nw72NdX757gdWT71BnBreOvcmkhKbz1hemuftc6/TWQh5+N+YjXsPKKxPicNkhqZXIpVGOkfuPALP\npyElSjkoLEhNoF7OWPZQfDtXe/EOxVJfe/Hq8BVVh6+oOnxF1eErqg5fUXX4iqrDV1QdvqLq8BVV\nh6+oOnxF1eErqg5fUXX4iqrDV1QdvqLq8BVVh6+oOnxF1eErqg5fUXX4ivofRk5ESrJTPP4AAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa7f57c0470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa7f57884e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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FOndOqPqc9DGJR175ROUKf/JHr+U5V16Lp3qp1g+CP8F5F64kNm0atQhjLKU+iV8QLF/0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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa7f5752518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RhhgEQjj02gn9do9izuW+5gzn3zRmvx8TxQ+RmRGeCXCtgTQjiqC1uMDMzDza\nZCTjAa7j4HkV3vNNH+aGXmH4+CRFUjAmxjcBGoOUEiQoOaIsamjfopUmJ8NzIrw8IndS4jyhP+hT\n6BHjfJ/cGRAYF9+rIJXEcyoM44IPzv4AN7NbXOPX8WSfZlAh8BQPnr7ExfsuEpUCktjyuU8/w1N/\n8Di7nYNj4/lEeHahC5I0Z33jgN3dPuOkoFypMDs3SW/QplULESpna2+PxO+yO/M7CC04/EIxxgx3\nmZhtMTnbwvGgP+gyToakeQxIFqbOkpzaYurtmmowQz2YpOI2KbsTBCrCVQ6t0mlmapfZi68Tt7do\nj3YZ6RE5Oe1xl+5BF5PF7Ce3EUFCd9gh8pocLhGVtONdxnpI1xRM+1OUizkGzpBRlGEnMu5+6HAn\nCYXD1uY+f/zYU2zu7B1uN3tMOBGerbUlSTO2NzfYvn0T4dzD4nyL+blpFpZmmD+zyLA7YmtzC5yQ\nUj3EOgZbWOR4xOz5eU6fP0MUBVy7dY3nr17lwl2XcF0f3/dxXJcH3/Qwn1eP8fwLA6Y7l3io+S6M\nVmQmQzgu3qUDnsl+mZnly1SzKYQLN1afJ3InCa1HnO9zYG4xX72M1ZZQDVEE5Fog84w86aPcGoWf\nEbkRby19lI+v/BC1rODdb38Ts6eWMGHA9m6Hay9eI7Nt/LJCx8e3W9WJENtYTVFodJaxeesmrbk6\nzVaZSqXMxcvn8cKQrZtrDEYDJhfniMohnlOCwjJ5cYGF003Cckiv12V1dYOVF24yX5klr5fJsjEi\nhGZjnvrEBc6/+wYbLz3BQekW9fA0jiNpb6bsx0PWn1DMtAbENsIOU4STYvIE6Spyk1CJKlTCCVb3\nrxPIGsqJsAaQhxMS+sWAiixhpSGxbc6ffSeTjV0WZmYwvkOqDXu3tzi4vc5kKySb0iSD19l7dlEU\npOOYcTLCjEfsbFxjZnaKZqvO+bN3Y3Ofvc46UcnQdBTeuGCYx8yfmefchTpRqcRwMGJ3d0Bno814\nN+b2tWUacw28YIh0BIFb4p4rd7Ox9gRnZ2epyjI6cUl7DrIy5taTfeL4gNvrQ+465UFh6A5TZqIq\nm3GXQm2h4hKb6SoldxowKNcyiMdEng+OQpucQd6nxz4L5Uu8/xsfxauvUljNMEsZ7Q65/afLyE2N\nTwVRLZg4fXxz0E5Gm10UZGlCniR4jkc86HLQXmc4HOKqMnkB2JiZKCDoxRysbVOtl5lfKFMqlSl0\nTq87ZNhPGewPGR0MWLm5xrAzxKDJixSJolxp4CDoDF9ksD1LM5wmI2NjpyCOdxC4gOTayovsDtYA\nQSfeJWGbuYlzOHlBpCI8HIoGgZr4AAAS70lEQVQ8xxoF1hAnMWk+xJBgZM60u4g0BR//t4/RHTUZ\nVycZZ5rN5Q3i3Q6ucAlsAHsu/duvs9ml2hiKLEEpgwgFad+yvXqDUtSkUXEYjYcs+RM0yhHXb+1i\nWi3uumeeeqtCmid0umN6nZTt9TW2rt3AZJr95Q2efOw5Hp2fA5lR2AJXebzp/g/xiSf/iImzLV7u\nrXB95xq9jRJKVNBOjjEFAujRoSmbTExE1JwFBvEyC42HyNF04n3AoDMXaSX9pENud1FCU3Vn0YEh\nD4Z09pf5mb//R4cTH4IIH82p0xGhtHhSUvciev342Hg+EZ6tjTl8JRpl5LogHafIsYssShgESrlM\nemWSUYJtVbn88H00ak20zul0enQ7Y3p7bTbWVtCjMRKYvrBAwynx2Z97kd0XehRFhjCWSrjAVHOJ\nffkyW+YmO90VUrWCkTGWMYYYYTLuffcDuKWI/dEX6OhPoyoJ2gyxZCiVYzEMdQdtEpK8Q27ajLJd\nxmaDre5LPL/yNMkowBEhjighspQ8lSzefw868MFYFFAKjm+7pBPh2YbDYT89yujsxITCR52bxAka\nWCMQTkFaclkTmsV3vp35xRmsKOh2h2ysjeh2+9x8+Vm2r9/Gnarylnfdy0NvvRvZm+Lf/OTvcPV3\nNFGtzuIFh7CZI2aWScopVh+OYydFHzca4Lst4p7GFpL8mYC0u4otDckdYNjEDwyIAIcyeRwzNBvI\nwiGzeyinh5QOyroIz5IOY0JVJfOqiEJjbICWI669sME7PvwBrn7iMfLtfXz3dSa2NRaDoSgKHH24\nyD2oN3CCgDRPGMVDzETIxMVz1FtNHM+lN+iztdlnf79PZ3uX7du3aZ2a5G3vvY/TZxZpTU6R7URY\n3+PmyhNcWZ4jW5rAq5Q4/eCb2dxuUzUD/ChCjzOk5zJ/9wU8Ybn++RX0AKwqOBV8HVncZhyXebH3\nPPfPvh3PncA4OeSC1OzjlmLKykMVPtJzsJHEzxXS+HhuhOM6pHmPmn8eNXYJI5e73v5WbnzuScYb\nu8fG88kQWwswAlFYLs5M0JydY3pugcCPiIcxt27eZm7BozldphCa8TBh+cYOe3tj+us77G6v8N4P\nvpXzF5aYm13EC3w2n76J6UZcOfUgkXFZfuaPcaIxZ9/7EJcuPsDO1mdo1CdxIw/VU9TqLZRb0O4c\nIKI9dvvP4PkuN72P47anSEcewh+yeXCVyzMfoKPW0CoGd8zU5SrrK9eYW1ii1QxZ7j1Jz93h1Jl7\nkRtTpNspVmZkxT5by6vw2we87WvfxX1f+y6+8Jknjo3nEyF2lhVIK1BaMVUtMbO4RFCdQqmQUdKh\n3+mxuDBFw0jMKGOzf8D2dgc7Mjih4eH3P8g9952hNlFHSI/d7S5FL6cuc2YmJ8mz01ih2bi6wtkH\nH8CWLa6jDje3qVUY7rUphEO/P2Sw10HH0C3dgtDj7sYV3FrCW+97jq2tGn/4RMIT65tUozNMX/So\nXbxAe9RldG2AVClbe39KrHpEdZdgMmPmdI/rn91hvHE4mKIcy+Zzz/NcOeDCffdx8aG7j43nEyF2\nXliU51GuVIitg6zP4UcVrFEMxgkLZ+epNOrIVONtjUi29kjjMQuLU9x1YYlmaxI/CDBGsXp7hxef\nX2b5mZt86K0f4oGHpjjoPE42rjDY7vLxH/84lcs1Ji9Ms7O9zt1nTvHk9j7SFYyHPZLu4TId40At\niLiw8O2kYpcHPvD1nGpfZ/NgFWmmkKJJ64MjBt0aYtAmDH36wyHDYQ9Z05RnPKJIsXZjl92dNQoD\nSoU4aFypaHgpLzzxR5y69MCx8XwyeuNCUmo2aJ2dZxyUMX4ZaRyskXieS22igvQ8hhIGwxhPF1y4\nMMu5yzNMNGbwvRLJOGdvr0+3p6nW6lx520W23C1ubCzzjkcepcj7REGGckeo3hBpLEmSceXUeU4t\nLiIchY4T0BpTZDhVj1JpgbVb+9x1aoaw9k42th6gPvlhRlmF2Uc0jfo85UoFUxxOImz3OqSZJos1\nbikAIUgOEkAhHANqfDiLFmjML0LqsL/zVf2N2FeFE+HZUguQHjPnzlDEGicqkRQ5WuvDKbi+Ik81\nN5fXad/e5HzrLDc/XmNHBGynz/Hhv3U3WzttkhiEhHIlRE34YDUd3WOYXufUg2/DLwR7Nza4tbJO\nL7mFN+9RCn3ef+/9/Pwn/oh8lJLmCcoJWTLfgttJWd19idOrs+y1LXnmcfPqBm5FcP2ZNg9dbiCF\nIM9TdG5BpYDCjCAQFXSWMU4HeCUFQuBKhZQKqyT16VMId5W7LvxF/kbsz4cTIbYQEo1EuC7l1gSO\n55Pnh8OPni8QSnGw22Xj5i10v4aQ76Um+6xtbTPKJH/4Mzeo3KtxA49y1cP1PBAGJDi+QjoppRkP\nEzu4ssFEZUCaatwDl7WVl3h44QKBNewOBxQ6pxFdohjtoanhy0kuPexTaXh86g+fZn+8hadqnA8W\nsHiH22Lm+eEaMHm4s4IQAkcodJExNglFDkopfMelUqnQmJvGcytUJ6q05lrHxvOJENvzXIbJGCkE\nRZGRFTFYHykF1gryTLO5soxu17h/9tvY6fUZ2h7CDkmG+7SiKZ5/8lkKr8D3XKrNKrOLTRqTdSab\nDbzAQ41GmJaHV6+x4DyEMoqXnrvGfkchXMmluTor68vMnjlL79YItzZGaMtYrPLPf/IWH/5r/xnX\nV56nXK6QssPABuj0FFam5CIjmnTxlIOQliLR+NbihQIncEm7GeQWLaHilVg4fRZjBacunKHWrB4b\nzydCbCXBkc7h0ldtiMcdomAGOJzFEg8GOL5kYi7ic8/9AaODMZ5sEOUBQVly+vQMaxtt3EbGS8tf\n4NpTzzA9N8XchUXMQxeYmZ1iQmmMBeOFFBT4nse5K/fzzFNf4OYw4Wvf9hCL73wbN652+PyNp5Aj\nieNa+l5M3O/xCz/3E3iBJC0StB5QqS8hCnADRXUyIM08pAfFIEOnhisX34Jbsrz03CZjlSMseJ5H\nEJYoVycptKbaKON4x9dtOhEdNEuO54aAg5UOWZGR5V2MzhgnY7q9HaZnTvGWR96FjVP29pcJTYiT\nR+y3+6zfPsV9sx+lsfJBWtvv5aEHvw4VBjz3iaf4tX/2MX79V36X9b1tOr09HOEhhUAbQ6ni0ZqY\n5NnukPm5Wd7+pss88W9vIB2HxI8ZeUMcVxJWXYSypDomLzq4osLFU/PUPZ+oJGhNtrAZFKMM3QOX\ngAv3vIeJ5hX8Sohf9nAdAVjwPVQ5QvpQbkTwZ/6d6H8anAjPRhcoqXCEPNw2CsU4jxFCodMCcpdK\nZY64Z7hw6u3kiaI9vEk3buPi0+vsc3rpEiVTp9Gq8vTmY7z/r55hf7eDlgOmWk1kJNlor+NEM4eL\n79FgDROTdbZ3NtjraCqVMYt319h5NqcYx+TkFDImyYZoUyDQNCrn8d0yp5sNuqZAKQgrFbIO6Cwn\nEiXe/A3vxQ0ihnsWqQRh2SVN7eF+adUAXAc3Aq8ksMc3nH0yxDYWOvtbeF4VodS/25tkND7ASx3q\n5RkqpQa5M2ZGLRLMTvG7n/8JhJ/QaC6xu9vFWarQvJCz+7zm4tm38Pn/+xpZUuejP3I/XqTodQ/Y\n2t9ld3+PhbnTCGUx0jI3Pc3m+hafbe9ypT+mPBGwLSROFoCURF6JKGygtENUaiK9Mo985xk+9+QT\nbGUdTr/zIpVKhBtExN0+V952nrP33U+qC3ZWt0BK/IrLcHcMaKrTk1hr8AOJH0hK3uuszRYGRoM+\nlHz8KMAYkEKhtcV6glKthTaG3ZUBVsd88ulf4+y5NzF/aok/efz3GKkRTzz3ae59y2USNWB9bQXb\nO0AOPW79yhQlt8G1p3Pa9h763k2W7hszNd3C8RymT2umps/w4sufptsIybZTpBMhdIZFok2OJCLU\nNeq1S+Stm0S1Jr/y6cfQaCavLFCfrFJpRmSpy+kH7ycolUgyze7WFiIXOIGHFAojDZVGAysknuNR\n8So4r7dq3GqLMTnSSvygzCiL8RBI5ZFqQ07KaGvM2rWX6G3ucObCeW5tPs4LNz6NFwpCT/DUy7/F\nkzf+DY3ZGvsbCW+96z3c/75FrrwjwvMDrj8X4u2klJglHYc8sbdGLhO0YylNTJG6BdGjXT74N9+L\nTi0/+4MfI8kHKA1lp8TkzEW2Bi8yc2ZE5HlYLKKw7G3sUG+Wqc6XmJ69wvTSLFKFHOy1uXX9ZYxn\nqTQCytUQN4zwwyrWCmrlCQSScTY8Np5PRgfNWhASJRSRCqiYw9cuJSMQgm5vk9Vbz7G1eotyNMdb\nH/4A5868Dz/yuFB/P4++6fv50Ec+SqXmMzP5AEszl6hUGtzefBYvckhzwfTCGcLSIrmukkqFCQ/f\nwWsTVXK7x7Cdsb/V4+VrN3n8c7/N133DBVylKchQJcUw7zIWLyBdF8eVRJGP5yv62weMM4sXllm6\ncDeOGyKlZHtrlXGyhwgsfhBRaVY4dfYcrhMgpUCbnKwYo+TrzbOtwBEhQiokDoEeIpWHcQKsDSny\nMYXbx/El424fQUa15DJVvcSbH3w3Kom4/vTzmNSl277NIB3RGTaouA2e+WQVnQyJeztk2TpRMMaR\nULItOnqT9tYmD9/1IXrTn+Xs2RarLz9DOZ6gPlnjGx99B5vxJltbQ/rrz5HaDp5fQinFpfP3EynF\nSPfJhzGX736AWrkGSISRvHz1OUw2pui76IohrAZMLCxSWINQGqFylHRw5cSx8XwiPJtCo5RzOK5t\nNX1S+lmM0SCli1IBteY8977vbvbTz7G5/QTLuzepTTTojdu8fONFhntd3KLE9t7jDNJbOBNDZk/P\nsn57hbXN22wMXuSANcRkjlWC9ugGm4OrXL7yAOvxGmfONzg3s4DtlJieOcdqb5ukMuId3/IQ9339\naUal2wgESigshiRNGYxG9Do9xvs96tXm4f5mQtLv9di6cZOsXzDeyEgHCefuv5+p+XmMFngOOAoC\np0qeHZ8EJ8KzHdehNTlN4IcIqemkfaKghZTu4edICb5bwQ3h237wEVrNCTr/okvQPcvGzRvs93bo\nDLcIZUFjVrK3v06n/xzPXd2h1KrjBh6Z3kRRkKwNSd2EsBKwqC7y0tWnqZdq3HpB8jUP1pk7P81A\nbtK8Z4K7H7iIV3KpTTaRCJ74+JNMTrVIrOD2zm1Ge20CVzF33zmElWgr8Z2AldVrJIMBykC5GvL2\n/6+9e9lp4woDOP4/c/N4xhfAxhhBjGlLIQWSkEsbFFVpKhapmkqtukueoFLfqO/QqutWQpUSiV4W\njUScRKQxIQ4YO2MYX8dz7YJ9lzTSzO8FRjr/zZnF952Pb1NZfZ/jTkTYHZHNK6SlHCNH5tS2z++c\nz+1L/6GyWEVIoCkGe/u72N0TysUqKTXNsD8GfEDCGXkE0zJOBFsPqmz/8IyTXo9g3CKT8pAKDqFm\n4QYWR+1fUZQl3pv7hOVPV5iaLdIPO3S7b4naOmndpNO0GdsHBFmFr79fYVKoeGmLjn3K6PkYRTdY\nXlvANHSWLy8RhBE5s4gQAVpWw/VlzFyGmflZAulstMPpO7x93cDUFMa+x71vv6K8XmXkgev4CBGQ\nN3KcngY0m2/wvZhNcZ49QBrQ6w3Yq79AExrlUgnfl7HtAWEQ4kc+UShotbvIkko6ZbJ5/yJT3xkM\nBhZ/P60xOVSp/eVTnirROx6QycDIrbP92wuqy2XSEyrS4AT3QsTqxmVy2SqqugxSgCbJGGOozF9n\nHNRp1Hb58eHvfH7/S1YuVZguFLh+c42xM4HVGZGfyZOacvlo/RqTsyU8z0eRdV6/alCvPSWfM7i6\n9QVzGxU8xaHbEbjjEF3X+OfJIX882uHqzRXWbiye2zm/E7HrB01KpRT7jTqdE4vFhYtIUQZFFgjJ\nJnDPFpFJQsJxXCxrSLfZYOgNuXJtCavTprb3J6qvUc0XGPgK+YzEyBnQ70U09p9QKOhkC/MgIkBw\neHSEZR+T1nPomkleN9GyGaaMDIqkMF0ucLRX4+FP2wTOLa7fWiOTy0Ok4vktotAHZArF8tlWJz9E\nkWUOX9bp223u3LvDwuoFXEYEPjiOIAwFzcYxe4932Lp7g4WVGZS47VQ5Ou5gnUS03h5i6FkmcxVC\nP82b1ita1iF5s4hAReLsiQm71+Hg5S7ZvEkkFun2+5w0WxhpjcqHS7StPpblo+tZQsXHiwYIKYWZ\nmqQ77KEgoSoahhwxtJsM5IBTVaLYnkbKLKEpEmubV7mwVubZzmMe/fwLznDEpY11JsqzuP6QYqHI\naJDGMCcYez5CqPRP++w/r3Hls00WNj5AGCFe6OK7GuNxRAiYZsQ3D25TmpsEQoQ4vwuaiKLzGyxL\n/L/ejV+vxLlIYsdIEjtGktgxksSOkSR2jCSxYySJHSNJ7BhJYsdIEjtGktgxksSOkSR2jCSxYySJ\nHSNJ7BhJYsdIEjtGktgxksSOkSR2jCSxY+Rft5xobsTLeosAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa7f56c2e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_model(model_ft)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
